Abstract:Skyline queries are useful to multi-criteria decision making as they represent the set of all solutions that the user can safely take without fear that something better is out there. It can act as a filter to discard sub-optimal objects. However, a major drawback of skylines is that, in datasets with many dimensions, the number of skyline objects becomes large and no longer offer any interesting insights. To solve the problem, k-dominant skyline queries have been introduced, which can reduce the number of retrieved objects by relaxing the definition of the dominance. Though it can reduce the number of retrieved objects, the k-dominant skyline objects are difficult to maintain if the database is updated. This paper addresses the problem of maintaining k-dominant skyline objects for frequently updated database. We propose an algorithm for maintaining k-dominant skyline objects. An extensive performance evaluation using both real and synthetic datasets demonstrated that our method is efficient and scalable.